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Health Aff (Millwood). Author manuscript; available in PMC 2016 December 01. Published in final edited form as: Health Aff (Millwood). 2015 December ; 34(12): 2077–2085. doi:10.1377/hlthaff.2015.0685.
The Early Impact Of The ‘Alternative Quality Contract’ On Mental Health Service Use And Spending In Massachusetts Colleen L. Barry, Associate professor and associate chair for research and practice in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, in Baltimore, Maryland
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Elizabeth A. Stuart, Professor in the Department of Mental Health, Johns Hopkins Bloomberg School of Public Health Julie M. Donohue, Associate professor in the Department of Health Policy and Management at the University of Pittsburgh Graduate School of Public Health, in Pennsylvania Shelly F. Greenfield, Professor of psychiatry at McLean Hospital, in Belmont, Massachusetts Elena Kouri, Project director in health care policy at Harvard Medical School, in Boston, Massachusetts
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Kenneth Duckworth, Medical director for behavioral health at Blue Cross Blue Shield of Massachusetts, in Quincy Zirui Song, Physician in the Department of Medicine at Massachusetts General Hospital, in Boston Robert E. Mechanic, Senior fellow at the Heller School for Social Policy and Management, Brandeis University, in Waltham, Massachusetts Michael E. Chernew, and Professor in the Department of Health Care Policy at Harvard Medical School Haiden A. Huskamp Professor in the Department of Health Care Policy at Harvard Medical School Colleen L. Barry: [email protected]
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Abstract Accountable care using global payment with performance bonuses has shown promise in controlling spending growth and improving care. This study examined how an early model, the Alternative Quality Contract (AQC) established in 2009 by Blue Cross Blue Shield of Massachusetts (BCBSMA), has affected care for mental illness. We compared spending and use for enrollees in AQC organizations that did and did not accept financial risk for mental health with enrollees not participating in the contract. Compared with BCBSMA enrollees in organizations not participating in the AQC, we found that enrollees in organizations participating in the AQC were slightly less likely to use mental health services and had small declines in total health care
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spending, but no change was found in mental health spending among all users. The declines in probability of use of mental health services and in total health spending attributable to the AQC were concentrated among enrollees in AQC organizations that accepted financial risk for behavioral health. Interviews with AQC organization leaders suggested that the contractual arrangements did not meaningfully affect mental health care delivery in the program’s initial years, but organizations are now at varying stages of efforts to improve integration.
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Rapidly increasing health care spending and poor coordination and care quality are key concerns of policy makers and payers.[1] The predominance of fee-for-service payment, which provides incentives to deliver more care and leads to fragmentation in financing and delivery, is often identified as a barrier to addressing these issues. In response, there is broad national interest among commercial and public payers in new models of paying for and delivering health care services. The Affordable Care Act (ACA) and state-level reforms such as Massachusetts’ Chapter 224 of the Acts of 2012 encourage the creation of accountable care organizations and the use of bundled or global payment approaches with performance bonuses to reward improving quality of care as part of a strategy to control spending growth and improve value.
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One of the earliest attempts to innovate in this area is the Alternative Quality Contract (AQC), established in 2009 by Blue Cross Blue Shield of Massachusetts (BCBSMA). The AQC pays provider organizations via a risk-adjusted prospective payment for all primary and specialty care provided to a population (that is, the global payment or budget) for a fiveyear period. AQC organizations are eligible for bonuses, in the initial years of up to 10 percent of their budget and later as a per member per month amount, based on performance on sixty-four outpatient and hospital measures. The measures are described in detail in the online Appendix A.[2] As of 2015 twenty large provider organizations, representing 90 percent of BCBSMA network primary care physicians and caring for more than 650,000 enrollees, are covered under this arrangement. Research on changes in spending and quality under the AQC has found that the initiative was associated with a slower rate of total spending growth (driven primarily by a shifting of outpatient care to providers with lower fees and reduced use of procedures, imaging, and tests) and improved ambulatory care quality compared to BCBSMA members not enrolled in the AQC.[3–5]
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No information is available on how this model affects care for people with mental illness. Mental illness often goes undetected or undertreated in primary care, and mental health services delivered across primary and specialty care are often poorly coordinated under feefor-service arrangements.[6] The AQC model could improve mental health care by giving providers an incentive to bridge the historical separation of mental health financing and delivery from the rest of health care delivery. Also, mental health conditions are highly comorbid with other chronic conditions, such as cardiovascular disease, diabetes, and cancer.[7–10] Better integration could improve treatment of both mental health and other conditions among individuals with a mental health conditions, who often receive worse care for physical health conditions than the general population.[11,12] Finally, prior research suggests that better mental health care holds promise for improving the value of health care expenditures.[13–15]
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However, provider organizations at risk for a population’s total costs may avoid enrolling or underprovide services to high-cost individuals such as those with mental illness.[16] Bonuses for meeting performance measures are expected to encourage high-quality care and prevent stinting of care. The contracts for the period we studied included only two mental health measures, both related to antidepressant medication use. Importantly, the AQC’s effects on individuals with mental illness may depend in part on whether mental health services are included in an AQC organization’s risk contract. Only a subset of initial five-year AQC contracts (five of twelve) included risk for behavioral health (i.e., that is, mental health and substance use disorder) specialty service use.[17] (We do not examine use of substance use disorder services which, like mental health services, were included in a subset of the initial five-year contracts.)
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This study used a mixed methods approach. First, with BCBSMA claims data, we examined whether AQC implementation affected mental health service use, mental health spending, and total health care spending among enrollees diagnosed with mental health conditions. For all utilization and spending outcomes, we compared two intervention groups–one that included enrollees in AQC organizations that accepted behavioral health risk and one that included enrollees in AQC organizations that did not–to a comparison group of BCBSMA enrollees not participating in the AQC.
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Second, given prior evidence of worse chronic care management among individuals with mental health conditions compared with others,[11,12] we examined how the AQC affected five performance metrics for diabetes and cardiovascular disease among people with these conditions and a co-occurring mental health condition. Third, we interviewed leaders in both AQC organizations and specialty mental health provider organizations to gauge perceptions about the impact of the AQC on enrollees with mental illness.
Study Data And Methods Administrative Data And Study Population We used 2006–11 inpatient, outpatient, and pharmacy claims data from Blue Cross Blue Shield of Massachusetts. Our study population included adults ages 18–64 who were enrolled in a BCBSMA health maintenance organization or point-of-service plan, all of whom were required to select a primary care physician upon enrollment. A BCBSMA enrollee was eligible for the Alternative Quality Contract group if his or her primary care physician belonged to an organization that had entered the contract.
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Provider organizations could enter the AQC on a rolling basis in 2009, 2010, or 2011. We used a difference-in-differences design that included in the comparison group both enrollees in organizations that, in a specific calendar year, had not yet entered the AQC but would in a future year, and enrollees in organizations that never entered the AQC. The difference-indifferences approach allowed us to account for differences in the characteristics of enrollees across AQC and non-AQC organizations and secular trends in service use unrelated to the AQC.
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Identifying Mental Health Service Users
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We employed a common approach to identifying mental health service users in insurance claims.[18,19] First, we classified enrollees as mental health service users based on diagnostic codes 290, 293–302, and 306–316 in the International Classification of Diseases, Ninth Revision, Clinical Modification. An enrollee was considered an inpatient mental health user if the last primary discharge diagnosis and the majority of all primary diagnoses during an inpatient admission were mental health diagnoses. An enrollee was considered an outpatient user if he or she had a mental health primary diagnosis on an outpatient claim. The unit of analysis was the person-calendar year. An individual’s data for a given year was used if he or she was enrolled for medical, behavioral, and pharmacy benefits during all twelve months. In counting mental health spending and use, we included all mental health inpatient claims as defined above, all outpatient claims with a mental health primary diagnosis or a Current Procedural Terminology or Healthcare Common Procedure Coding System code specific to mental health treatment, and all psychotropic prescription claims for individuals identified as mental health users. Measures We first examined the relationship between the AQC and the probability of using mental health services. We then assessed, among mental health service users, the relationship between the AQC and average mental health spending, total health care spending, number of inpatient mental health days, number of outpatient mental health visits, number of psychotropic medication management visits, number of psychotherapy visits, and number of thirty-day-equivalent psychotropic prescriptions.
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For enrollees with diabetes or cardiovascular conditions with and without a co-occurring mental health condition, we examined four Healthcare Effectiveness Data and Information Set (HEDIS) diabetes management measures–LDL cholesterol screening, HbA1C testing, monitoring for nephropathy (kidney disease as a result of diabetes), and retinal exam–and one HEDIS cholesterol management measure–LDL cholesterol screening–following HEDIS specifications. We compared these metrics for AQC and non-AQC enrollees. Statistical Analyses
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We used two-part, difference-in-differences models to estimate changes in the probability of mental health spending and use attributable to the AQC. In the second stage, we estimated the AQC’s effect on spending using linear regression.[20] For intensity of use outcomes, we estimated negative binomial regression models. We used logistic regression to examine the probability of mental health service use and of meeting HEDIS-based performance metrics for management of diabetes and cardiovascular conditions. We estimated two main regression models: the overall effects of the AQC and the effects among enrollees in AQC organizations that did and did not accept behavioral health risk. We estimated a third set of regression models including cohort indicators to examine whether the effects of the AQC differed based on how many years the enrollee’s provider organization had been subject to the AQC. For example, in 2011 some enrollees had been part of an AQC organization for one year (that is, enrollees in organizations that entered the
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AQC in 2011) while other enrollees had been in AQC organizations for multiple years (that is, two years for enrollees in organizations that entered in 2010 and three years for enrollees in organizations that entered in 2009). In all models, we controlled for sex, age category (18–27, 28–37, 38–47, 48–57, and 58–64), gender*age, risk score (calculated by BCBSMA from current-year diagnoses, claims, and demographic information using the diagnostic cost group scoring system DxCG Risk Analytics from Verisk Health), AQC group cohort dummies (that is, whether the enrollee’s provider organization entered the AQC in 2009, 2010, or 2011), and calendar-year dummies. In all regression analyses, we clustered standard errors at the practice level. The magnitude of the results can be interpreted as the average annual change in outcome over the study period if all enrollees were subject to the AQC versus if all enrollees were not subject to the AQC.
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Given that our design was quasi-experimental, we used propensity score weighting to minimize selection bias.[21] In particular, “inverse probability of treatment weights” were created for each calendar year, equating the AQC risk, AQC no-risk, and comparison groups on observed covariates in each year.[22] Covariates in the propensity score models were age group, sex, and the risk score. Analyses using the mental health service users only used weights estimated among that subgroup. The HEDIS diabetes and cardiovascular analyses that used all enrollees with diabetes and cardiovascular conditions used weights estimated among that larger group.
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We ran several sensitivity analyses. In addition to our primary spending model using linear regression (a decision based on model fit diagnostics), we estimated spending using log transformed and gamma models. For outcomes examining quantity of services used, we also ran a Poisson regression as an alternative to negative binomial regression. Results for sensitivity analyses were nearly identical to the main results. See results available in Appendix B.[2] Semistructured Interviews
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We conducted semistructured interviews with senior administrative and clinical leaders at AQC organizations and specialty mental health provider organizations in Massachusetts to assess perceptions about how the AQC is affecting care for individuals with mental illness. We developed interview guides for AQC participants and specialty provider organization participants. Guides began with broad questions to elicit participants’ open descriptions of AQC implementation and potential impacts on enrollees with mental illness and then turned to specific questions regarding strategies, facilitators, and barriers to altering quality or spending for this group. We included qualitative data collection in this study to contextualize our empirical research and aid in interpretation of findings since AQC implementation involves complex organizational behavior.[23] We interviewed a total of twenty-five individuals with eighteen participants interviewed from eight AQC organizations (three that accepted behavioral health risk and five that did not), and seven participants interviewed from large specialty mental health provider organizations. All interviews were conducted between February and June 2014 by teams of two to four study authors with participant consent obtained. Detailed field notes were compiled and used to identify themes and findings.
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Limitations
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A number of study limitations are worth noting. First, given that we studied a single global payment and accountable care model affecting the privately insured enrollees of one insurer (albeit the largest) within a single state, our results may not be generalizable to other private or public payers or populations. Second, entrance into the AQC program was voluntary, and it is critical to consider the extent to which providers or enrollees in participating programs differed from those in nonparticipating programs. When comparing the trends observed for each AQC cohort during their pre-AQC time period to the trends for provider groups that had not entered the AQC by that time, we see no evidence of differential trends in the probability of mental health service use nor in total health care or mental health care spending among mental health service users. Even if there were slight differences in those trends, the estimation procedure would account for these pre-period differences by assuming they would have persisted. Third, our data end in 2011, and we did not study effects through the full life of the initial five-year AQC contracts; this choice was necessary to preserve a viable control group. Fourth, while organizations that did and did not accept behavioral health risk did not appear to systematically differ in obvious ways (for example, size or level of integration), it is possible that there were unobserved differences. Fifth, because of relatively small sample sizes, we may have lacked statistical power to detect small differences in HEDIS indicators for diabetes and cardiovascular care for mental health service users. Finally, while data provide rich detail about an enrollee’s service use, they are limited in their ability to convey important clinical detail. The claims data component of this study was determined to be exempt, and the interview data collection was determined to not be human subjects research by the Harvard Medical School and Johns Hopkins Bloomberg School of Public Health Institutional Review Boards.
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Study Results Usage And Spending
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(Exhibit 1). We found a significant but very small decrease in the probability of using mental health services (−1.41 percentage points) among BCBSMA enrollees in AQC organizations relative to the comparison group. This decline was concentrated among enrollees in AQC organizations facing behavioral health risk (−2.09 percentage points) (Exhibit 2). We found no difference in the probability of using mental health services in the no-risk group relative to the comparison group. We found no effect of the AQC on mental health spending for mental health service users overall or in the AQC behavioral health risk or no-risk groups. However, a 1 percent annual decline attributable to the AQC in total health care spending was detected among mental health service users overall (−$189), and among enrollees in the behavioral health risk group (−$238) (the decrease in the no-risk group was not significant) (Exhibit 2). Results in Exhibit 3 on annual changes in the average quantity of mental health services used attributable to the AQC were consistent with spending results. No differences in average inpatient mental health days among users were detected. We found no relationship between the AQC and the quantity of outpatient mental health visits among users overall.
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For the analysis separating out the effect on AQC organizations that did and did not accept behavioral health risk, we identified a statistically significant annual increase in outpatient visits among users in the no-risk group (0.51 visits). We found a significant, but small, increase in the quantity of medication management visits overall (0.08 visits) and within the risk group (0.09 visits). The AQC had no effect on the quantity of psychotherapy visits among service users overall. However, we found a statistically significant increase in psychotherapy visits among users in the no-risk group (0.48 visits). The AQC led to an increase in the average number of thirty-day-equivalent psychotropic medication prescriptions overall (0.23), and in the no-risk group (0.34). Sensitivity analyses in Appendix B looking at the effects of the AQC on use and spending by years of exposure did not suggest differential patterns by the years a provider organization had been an AQC organization.[2]
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Diabetes And Cardiovascular Disease Performance Measures
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Exhibit 4 indicates that non-mental health service users experienced statistically significant improvements attributable to the AQC in three of five performance measures studied (nephropathy monitoring, LDL screening, and retinal exam, among individuals with diabetes). While other research has shown that individuals with mental illness often receive poorer quality care for chronic medical conditions than the general population,[11,12] this does not appear to be the case among BCBSMA enrollees. Rates of compliance with diabetes and cardiovascular performance metrics in the non-AQC group among mental health service users were comparable to, and in two specific cases (that is, nephropathy monitoring and retinal exam) very slightly higher than, non-mental health service users in the non-AQC group (Exhibit 4). However, it appears that non-mental health service users improved more under the AQC compared with mental health users, who appear to have experienced no statistically significant increases in rates. For two measures, nephropathy monitoring (difference-in-differences −2.90; p value = 0.01) and retinal exam (difference-indifferences −2.57; p value = 0.02), the p value of the interaction indicates that non-mental health users benefited more from the AQC than mental health users, although from a clinical standpoint, the changes observed were quite small in magnitude. Perceptions About Effects Of The AQC
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Consistent with the quantitative findings described above, the central message conveyed from interview participants was that the AQC had not yet meaningfully affected the provision of mental health care to enrollees during the first three contract years. There was a general recognition among most participants that many of the enrollees with the highest total health care costs had mental health conditions and that their organizations needed to do a better job integrating behavioral health to perform well on the contract. However, the overarching view was that little progress had been made with regard to mental health integration during the contract’s initial years, and delivery system changes that would facilitate behavioral health integration were viewed as a longer-term objective. Among specialty mental health organizations, there was low awareness about the AQC. Participants noted several possible contributors to the minimal initial impact of the AQC on mental health care. Participants emphasized the limited capacity of the AQC mental health
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performance measures to shift behaviors. For AQC organizations, BCBSMA enrollees covered under the AQC constituted a relatively small share of their total patient populations– typically less than 15 percent. Given that there were so many performance metrics both within the AQC and across other contracts (including Medicare Pioneer and other commercial contracts), organizations ended up focusing on a small subset of measures that were common across contracts and perceived as “movable,” meaning that leaders felt they could improve performance on the measures with a reasonable amount of effort; other measures were largely ignored. None of the AQC leaders interviewed reported that their organizations had focused on the two mental health antidepressant continuation measures. Participants reported that clinicians in their organizations did not view these metrics as meaningful indicators of quality. Also, there was a three-month lag in receipt of prescription data by the AQC organizations, rendering the antidepressant measures “not actionable” with physicians. Also, these measures were viewed as applying to a relatively small number of patients compared with, for example, a mental health screening and follow-up measure (not included in the initial contracts but added in 2013 as a test measure), which was considered more promising.
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Second, in the face of dramatic change in financing and delivery created by the AQC, many leaders reported that their organizations were focused initially on improving “code capture” to ensure that they were accounting for what was already occurring within the organization related to performance metrics and were beginning to put in place new processes (for example, registries and reminder systems) to improve performance scores that primarily affected other clinical areas. Near the end of the first five-year contract period, participants noted that several organizations were in the early implementation stages of efforts to improve integration, including pilot programs to embed behavioral health clinicians into primary care practices and targeted programs focused on improving care for individuals with co-occurring depression and physical health conditions. One AQC organization mentioned that it had had recently instituted a pilot program involving chronic care case management for enrollees with diabetes and co-occurring depression that included a mental health professional on the diabetes care management team. Several early efforts relied on grant funding to cover salaries of new personnel (for example, social workers embedded in case management teams) and implementation costs. One stakeholder noted that the confluence of risk-based contracting across payers from 2012 to 2014 helped to justify these infrastructure investments to organization leadership.
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Finally, limited mental health provider capacity was viewed as a major barrier to change under the AQC. While prior research indicated that about half of the overall savings to date from the AQC has come from referrals to lower-cost providers, AQC organizations indicated that this strategy was less viable in the mental health context given the shortage of mental health clinicians accepting commercial insurance.
Discussion New payment and delivery system innovations are proliferating under the ACA and statelevel reforms, and these models have the potential to address some long-standing concerns about care for mental illness. In this study, we examined how one model launched in
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Massachusetts–the Alternative Quality Contract–affected access to mental health services, the quantity of services used, mental health spending, and stakeholder perceptions of the AQC’s effect on mental health service delivery. Our findings suggest a small decrease in the probability of using mental health services attributable to the AQC concentrated among enrollees in AQC organizations accepting risk for behavioral health. We found no effects on mental health spending among users, but total health care spending among this group did decrease consistent with prior findings in the full enrollee population.[3–5] As noted above, prior studies found that overall spending decreases were accompanied by a shift in referrals to lower-cost providers. Shortages among mental health providers or weak referral networks might explain why no reductions were found in mental health spending among users. In the presence of these shortages, a substantial reduction in mental health spending could have signaled possible stinting.
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We found changes in the quantity of services received among mental health users in the norisk group, including relatively small increases in the number of outpatient services, psychotherapy visits, and psychotropic prescriptions. There was less evidence for effects on quantity of services used among the risk group, although small increases in medication management were found. Since BCBSMA manages inpatient care for all provider groups (AQC or not), it is perhaps not surprising that no changes in inpatient use were detected.
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The AQC did not lead to significant improvements in performance metrics for diabetes or cardiovascular disease among enrollees with co-occurring mental health service use, although performance rates in the absence of the AQC approached or exceeded 90 percent for all measures but one (retinal exams), leaving only limited room for improvement. For two measures–nephropathy monitoring and retinal exam–it appears that non-mental health users benefited more from the AQC than mental health users. Given research showing serious chronic disease risks including high rates of premature cardiac death[24] and diabetes-related complications[25] among people with mental illness, population-based incentives such as those in the AQC might not be sufficient for addressing the needs of this highly vulnerable group. More targeted monitoring and customized interventions may be needed to better manage chronic diseases among the subgroup with co-occurring mental illness.
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Qualitative study results indicate that participants perceived the AQC to have had a minimal impact on care for mental health during the study period, achieving little progress toward integration of mental health and physical health care. However, several AQC leaders mentioned efforts in the planning and early implementation phase to improve integration, particularly for individuals with co-occurring mental and physical health conditions. These qualitative findings are consistent with the results from the claims-based quantitative analyses and with results from a national survey of accountable care organizations, which found that only 14 percent of respondents reported that integration of behavioral health and primary care was complete or nearly complete.[26] Prior research indicates that the AQC led to an average annual increase of 2.9 percentage points in the two-year period following AQC implementation for one of the two antidepressant measures tied to bonuses.[5] One possible explanation mentioned in multiple
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interviews was that the increase could have been as a result of improved coding since programs aimed at improving depression care performance had not yet been instituted by AQC organizations. Lack of focus on improving performance on the mental health measures reported by interviewees raises questions about performance incentives in risk-based contracts. A global payment model such as the AQC places strong financial incentives on provider organizations to improve the efficiency of care and control spending growth. These models rely on performance incentives to discourage stinting and promote high-quality care. Given the numerous dimensions of performance improvement and the goal of measuring it broadly across many subpopulations and types of services, it is not surprising that only two of the AQC’s sixty-four measures–both well-established, validated measures–were devoted to behavioral health. However, the fact that providers did not feel these measures were meaningful indicators of mental health care quality and felt that the barriers to improvement on these metrics were too great underscores the challenges payers face in designing performance incentive systems given the lack of widely accepted behavioral health performance measures. The behavioral health field must continue to develop new, meaningful measures capturing multiple dimensions of performance with respect to behavioral health care delivery to ensure that measurement systems in risk-based contracts achieve their intended goals.[27]
Conclusion
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This study provides the first empirical examination of how this new model of paying for and delivering care affects individuals seeking mental health treatment. As accountable care evolves, it will be critical for payers considering these models, as well as providers operating under these contracts, to understand how they affect care for often high-cost individuals with mental health treatment needs.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
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Preliminary results were presented at the National Institute of Mental Health’s Mental Health Services Research Conference, in Bethesda, Maryland, in April 2014, and at the Association for Public Policy Analysis and Management’s Fall Research Conference, in Albuquerque, New Mexico, in November 2014. The authors acknowledge funding support from the Commonwealth Fund (Grant No. 20130499; Multi-PI: Colleen Barry and Haiden Huskamp). Shelly Greenfield acknowledges support from the National Institute on Drug Abuse (Grant No. K24 DA019855). Robert Mechanic is a trustee at Atrius Health, one of the provider organizations that signed the Alternative Quality Contract with Blue Cross Blue Shield of Massachusetts (BCBSMA). The authors thank Dana Gelb Safran at BCBSMA and Jeffrey Simmons, formerly of BCBSMA, for their support of the project, and Christina Fu and Hocine Azeni of Harvard Medical School for expert programming support. The authors also thank Alisa B. Busch for her assistance in developing mental health diagnostic cohorts and comorbidity categories, as well as mental health treatment categories derived from procedure codes.
Biographies Colleen L. Barry ([email protected]) is an associate professor and associate chair for research and practice in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, in Baltimore, Maryland.
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Elizabeth A. Stuart is a professor in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health. Julie Marie Donohue is an associate professor in the Department of Health Policy and Management at the University of Pittsburgh Graduate School of Public Health, in Pennsylvania. Shelly F. Greenfield is a professor of psychiatry at McLean Hospital, in Belmont, Massachusetts. Elena Kouri is project director in health care policy at Harvard Medical School, in Boston, Massachusetts.
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Kenneth Duckworth is medical director for behavioral health at Blue Cross Blue Shield of Massachusetts, in Quincy. Zirui Song is a physician in the Department of Medicine at Massachusetts General Hospital, in Boston. Robert E. Mechanic is a senior fellow at the Heller School for Social Policy and Management, Brandeis University, in Waltham, Massachusetts. Michael E. Chernew is a professor in the Department of Health Care Policy at Harvard Medical School.
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Haiden A. Huskamp is a professor in the Department of Health Care Policy at Harvard Medical School.
Notes
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1. Berwick DM, Nolan TW, Whittington J. The Triple Aim: care, health, and cost. Health Aff (Millwood). 2008; 27(3):759–69. [PubMed: 18474969] 2. To access the Appendix, click on the Appendix link in the box to the right of the article online. 3. Song Z, Rose S, Safran DG, Landon BE, Day MP, Chernew ME. Changes in health care spending and quality 4 years into global payment. N Engl J Med. 2014; 371(18):1704–14. [PubMed: 25354104] 4. Song Z, Safran DG, Landon BE, He Y, Ellis RP, Mechanic RE, et al. Health care spending and quality in year 1 of the alternative quality contract. N Engl J Med. 2011; 365(10):909–18. [PubMed: 21751900] 5. Song Z, Safran DG, Landon BE, Landrum MB, He Y, Mechanic RE, et al. The ‘Alternative Quality Contract,’ based on a global budget, lowered medical spending and improved quality. Health Aff (Millwood). 2012; 31(8):1885–94. [PubMed: 22786651] 6. Edlund MJ, Unützer J, Wells KB. Clinician screening and treatment of alcohol, drug, and mental problems in primary care: results from Healthcare for Communities. Med Care. 2004; 42(12):1158– 66. [PubMed: 15550795] 7. Ciechanowski PS, Katon WJ, Russo JE. Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Arch Intern Med. 2000; 160(21):3278–85. [PubMed: 11088090] 8. Singer S, Brown A, Einenkel J, Hauss J, Hinz A, Klein A, et al. Identifying tumor patients’ depression. Support Care Cancer. 2011; 19(11):1697–703. [PubMed: 20853171]
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9. Katon WJ. The comorbidity of diabetes mellitus and depression. Am J Med. 2008; 121(11 Suppl 2):S8–15. [PubMed: 18954592] 10. Carroll D, Phillips AC, Hunt K, Der G. Symptoms of depression and cardiovascular reactions to acute psychological stress: evidence from a population study. Biol Psychol. 2007; 75(1):68–74. [PubMed: 17196733] 11. McGinty EE, Baller J, Azrin ST, Juliano-Bult D, Daumit GL. Quality of medical care for persons with serious mental illness: a comprehensive review. Schizophr Res. 2015; 165(2–3):227–35. [PubMed: 25936686] 12. Kilbourne AM, Welsh D, McCarthy JF, Post EP, Blow FC. Quality of care for cardiovascular disease-related conditions in patients with and without mental disorders. J Gen Intern Med. 2008; 23(10):1628–33. [PubMed: 18626722] 13. Chiles JA, Miller AL, Crismon ML, Rush AJ, Krasnoff AS, Shon SS. The Texas Medication Algorithm Project: development and implementation of the schizophrenia algorithm. Psychiatr Serv. 1999; 50(1):69–74. [PubMed: 9890582] 14. Mumford E, Schlesinger HJ, Glass GV, Patrick C, Cuerdon T. A new look at evidence about reduced cost of medical utilization following mental health treatment. Am J Psychiatry. 1984; 141(10):1145–58. [PubMed: 6435457] 15. Katon WJ, Russo JE, Von Korff M, Lin EH, Ludman E, Ciechanowski PS. Long-term effects on medical costs of improving depression outcomes in patients with depression and diabetes. Diabetes Care. 2008; 31(6):1155–9. [PubMed: 18332158] 16. Frank, RG.; McGuire, TG. Economics and mental health. In: Culyer, AJ.; Newhouse, JP., editors. Handbook of health economics. 1. Vol. 1. Cambridge (MA): National Bureau of Economic Research; 1999. p. 893-954. 17. All second-round five-year contracts include risk for behavioral health service use. 18. Goldman HH, Frank RG, Burnam MA, Huskamp HA, Ridgely MS, Normand SL, et al. Behavioral health insurance parity for federal employees. N Engl J Med. 2006; 354(13):1378–86. [PubMed: 16571881] 19. McConnell KJ, Gast SH, Ridgely MS, Wallace N, Jacuzzi N, Rieckmann T, et al. Behavioral health insurance parity: does Oregon’s experience presage the national experience with the Mental Health Parity and Addiction Equity Act? Am J Psychiatry. 2012; 169:31–8. [PubMed: 21890792] 20. Buntin MB, Zaslavsky AM. Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. J Health Econ. 2004; 23(3):525–42. [PubMed: 15120469] 21. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983; 70(1):41–55. 22. Stuart EA, Huskamp HA, Duckworth K, Simmons J, Song Z, Chernew M, et al. Using propensity scores in difference-in-differences models to estimate the effects of a policy change. Health Serv Outcomes Res Methodol. 2014; 14(4):166–82. [PubMed: 25530705] 23. Patton, MQ., editor. Qualitative research and evaluation methods. third. Thousand Oaks (CA): Sage Publications; 2001. 24. Newcomer JW, Hennekens CH. Severe mental illness and risk of cardiovascular disease. JAMA. 2007; 298(15):1794–6. [PubMed: 17940236] 25. Frayne SM, Halanych JH, Miller DR, Wang F, Lin H, Pogach L, et al. Disparities in diabetes care: impact of mental illness. Arch Intern Med. 2005; 165(22):2631–8. [PubMed: 16344421] 26. Lewis VA, Colla CH, Tierney K, Van Citters AD, Fisher ES, Meara E. Few ACOs pursue innovative models that integrate care for mental illness and substance abuse with primary care. Health Aff (Millwood). 2014; 33(10):1808–16. [PubMed: 25288427] 27. Goldman ML, Spaeth-Rublee B, Pincus HA. Quality indicators for physical and behavioral health care integration. JAMA. 2015; 314(8):769–70. [PubMed: 26043185]
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Exhibit 1
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Unadjusted Descriptive Statistics For Adult Blue Cross Blue Shield Of Massachusetts (BCBSMA) Enrollees Using Mental Health Services In The Comparison And Alternative Quality Contract (AQC) Groups Across Person-Years, 2006–11 Comparison groupa
Total AQC group
AQC group with behavioral health riskb
AQC group without behavioral health risk
2,999,221
533,568
236,542
297,026
51.67%
52.75%
51.02%
54.13%
18–27
17.08%
17.26%
15.66%
18.54%
28–37
17.58
17.84
16.72
18.73
38–47
24.96
23.96
24.28
23.71
48–57
26.15
26.17
27.29
25.27
58–64
14.24
14.77
16.05
13.75
Any co-occurring substance use disorder service use
1.63%
1.99%
1.93%
2.04%
1.24
1.26
1.31
1.22
Any mental health service use
14.44%
16.13%
14.87%
17.13%
Any inpatient mental health days
0.23%
0.26%
0.25%
0.27%
Average number of inpatient mental health days among inpatient mental health service users
11.92
12.74
13.29
12.34
Any outpatient mental health visits
9.84%
10.17%
10.27%
10.10%
Average number of outpatient mental health visits among outpatient mental health service users
9.44
9.50
9.55
9.46
Average number of medication management visits among outpatient mental health service users
0.93
0.84
0.84
0.84
Average number of psychotherapy visits among outpatient mental health service users
7.46
7.80
7.85
7.77
Any psychotropic medication use
9.19%
10.26%
9.38%
10.95%
Average number of thirty-day-equivalent psychotropic prescriptions among psychotropic medication users (mean)
12.98
13.21
13.13
13.30
Average mental health spending among mental health service users
$3,151
$2,929
$2,918
$2,937
Average total health care spending among mental health service users
$7,783
$7,638
$7,979
$7,412
Number of individuals (person-year) Female Age (years)
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Risk score (mean) Mental health service use in a calendar year
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Spending in a calendar year
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SOURCE BCBSMA claims data, 2006–11. NOTES Cost estimates have been adjusted for inflation.
a
BCBSMA enrollees not participating in the AQC.
b
“behavioral health risk”
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Exhibit 2
Author Manuscript
Adjusted Annual Probability Of Use And Average Spending By Blue Cross Blue Shield Of Massachusetts (BCBSMA) Enrollees With And Without The Alternative Quality Contract (AQC) Across Person-Years, 2006–11
Author Manuscript
With AQC
Without AQC
Difference
95% CI
Probability of mental health use
16.04%
17.45%
−1.41%***
−2.06, −0.76
Behavioral health risk
15.21%
17.30%
−2.09%***
−3.29, −0.99
No behavioral health risk
17.14%
17.30%
−0.16%
−1.27, 0.95
Average mental health spending conditional on mental health use
$3,063
$3,078
−$15
−61, 91
Behavioral health risk
$3,030
$3,056
−$26
−126, 75
No behavioral health risk
$3,134
$3,056
$78
−51, 208
Average total health care spending conditional on mental health use
$8,137
$8,316
−$189**
−368, −9
Behavioral health risk
$8,078
$8,316
−$238**
−468, −9
No behavioral health risk
$8,207
$8,316
−$109
−300, 81
SOURCE BCBSMA claims data, 2006–11. NOTES CI is confidence interval. Two-part models adjusted for sex, age category, risk score, year, and AQC cohort and estimated using propensity score weights. Cost estimates have been adjusted for inflation. Difference-in-differences estimation used to account for secular trends.
**
p < 0.05
***
p < 0.01
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Exhibit 3
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Adjusted Annual Quantity Of Mental Health Services Used By Blue Cross Blue Shield Of Massachusetts (BCBSMA) Enrollees With And Without The Alternative Quality Contract (AQC) Across Person-Years, 2006–11
Author Manuscript
With AQC
Without AQC
Difference
95% CI
Average number of inpatient mental health days conditional on inpatient mental health use
12.52
12.13
0.39
−1.07, 1.86
Behavioral health risk
12.50
12.13
0.37
−1.35, 2.08
No behavioral health risk
12.55
12.13
0.42
−1.08, 1.92
Average number of outpatient mental health visits conditional on outpatient mental health use
9.54
9.35
0.19
−0.17, 0.55
Behavioral health risk
9.31
9.32
−0.01
−0.52, 0.51
No behavioral health risk
9.82
9.32
0.51**
0.04, 0.95
Average number of medication management visits conditional on outpatient mental health use
0.94
0.86
0.08***
0.03, 0.13
Behavioral health risk
0.95
0.86
0.09**
0.02, 0.17
No behavioral health risk
0.92
0.86
0.06
−0.01, 0.13
Average number of psychotherapy visits conditional on outpatient mental health use
7.01
6.93
0.08
−0.27, 0.44
Behavioral health risk
6.73
6.89
−0.16
−0.68, 0.35 0.02, 0.91
No behavioral health risk
7.37
6.89
0.48**
Average number of thirty-day-equivalent psychotropic medication prescriptions conditional on psychotropic medication use
13.21
12.98
0.23***
0.03, 0.42
Behavioral health risk
13.13
12.97
0.16
−0.09, 0.40
No behavioral health risk
13.31
12.97
0.34***
0.09, 0.58
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SOURCE BCBSMA claims data, 2006–11. NOTES CI is confidence interval. All quantity of service use outcomes conditional on an individual being a mental health service user. Negative binomial models adjusted for sex, age category, risk score, year, and AQC cohort and estimated using propensity score weights. Cost estimates have been adjusted for inflation. Difference-in-differences estimation used to account for secular trends.
**
p < 0.05
***
p < 0.01
Author Manuscript Health Aff (Millwood). Author manuscript; available in PMC 2016 December 01.
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Author Manuscript 91.55 62.99
HbA1C test
Retinal exam
87.31
61.45
90.40
87.45
90.36
Without AQC (%)
0.67
1.54
1.15
0.89
−0.94
AQC difference (%)
91.23
64.74
92.49
90.05
91.45
With AQC (%)
90.06
60.63
91.83
88.29
89.49
Without AQC (%)
−0.50
−2.57**
4.11***
1.17
0.49
−0.87
1.76*** 0.66
−2.90***
Difference in differences (%)
1.96***
AQC difference (%)
Non-mental health service users
p < 0.01
p < 0.05
***
**
SOURCE BCBSMA claims data, 2006–11. NOTES Logistic regression models adjusted for sex, age category, risk score, year, and AQC cohort and estimated using propensity score weighting. Differencein-differences estimation used to account for secular trends. Numbers for mental health users and non-mental health users, respectively: diabetes management: 26,113 and 157,023; cardiovascular disease management: 7,209 and 49,622. Non-mental health users are the referent group.
LDL cholesterol screening
87.98
88.34
LDL cholesterol screening
Cardiovascular disease management
89.42
With AQC (%)
Monitoring for nephropathy
Diabetes management
Condition management indicators
Mental health service users
Adjusted Annual Changes In Probability Of Blue Cross Blue Shield Of Massachusetts (BCBSMA) Enrollees Meeting Healthcare Effectiveness Data And Information Set Diabetes And Cardiovascular Conditions Management Indicators With And Without The Alternative Quality Contract (AQC), By Mental Health User Status, 2006–11
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Exhibit 4 Barry et al. Page 16
Health Aff (Millwood). Author manuscript; available in PMC 2016 December 01.
Health Aff (Millwood). Author manuscript; available in PMC 2016 December 01. Published in final edited form as: Health Aff (Millwood). 2015 December ; 34(12): 2077–2085. doi:10.1377/hlthaff.2015.0685.
The Early Impact Of The ‘Alternative Quality Contract’ On Mental Health Service Use And Spending In Massachusetts Colleen L. Barry, Associate professor and associate chair for research and practice in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, in Baltimore, Maryland
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Elizabeth A. Stuart, Professor in the Department of Mental Health, Johns Hopkins Bloomberg School of Public Health Julie M. Donohue, Associate professor in the Department of Health Policy and Management at the University of Pittsburgh Graduate School of Public Health, in Pennsylvania Shelly F. Greenfield, Professor of psychiatry at McLean Hospital, in Belmont, Massachusetts Elena Kouri, Project director in health care policy at Harvard Medical School, in Boston, Massachusetts
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Kenneth Duckworth, Medical director for behavioral health at Blue Cross Blue Shield of Massachusetts, in Quincy Zirui Song, Physician in the Department of Medicine at Massachusetts General Hospital, in Boston Robert E. Mechanic, Senior fellow at the Heller School for Social Policy and Management, Brandeis University, in Waltham, Massachusetts Michael E. Chernew, and Professor in the Department of Health Care Policy at Harvard Medical School Haiden A. Huskamp Professor in the Department of Health Care Policy at Harvard Medical School Colleen L. Barry: [email protected]
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Abstract Accountable care using global payment with performance bonuses has shown promise in controlling spending growth and improving care. This study examined how an early model, the Alternative Quality Contract (AQC) established in 2009 by Blue Cross Blue Shield of Massachusetts (BCBSMA), has affected care for mental illness. We compared spending and use for enrollees in AQC organizations that did and did not accept financial risk for mental health with enrollees not participating in the contract. Compared with BCBSMA enrollees in organizations not participating in the AQC, we found that enrollees in organizations participating in the AQC were slightly less likely to use mental health services and had small declines in total health care
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spending, but no change was found in mental health spending among all users. The declines in probability of use of mental health services and in total health spending attributable to the AQC were concentrated among enrollees in AQC organizations that accepted financial risk for behavioral health. Interviews with AQC organization leaders suggested that the contractual arrangements did not meaningfully affect mental health care delivery in the program’s initial years, but organizations are now at varying stages of efforts to improve integration.
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Rapidly increasing health care spending and poor coordination and care quality are key concerns of policy makers and payers.[1] The predominance of fee-for-service payment, which provides incentives to deliver more care and leads to fragmentation in financing and delivery, is often identified as a barrier to addressing these issues. In response, there is broad national interest among commercial and public payers in new models of paying for and delivering health care services. The Affordable Care Act (ACA) and state-level reforms such as Massachusetts’ Chapter 224 of the Acts of 2012 encourage the creation of accountable care organizations and the use of bundled or global payment approaches with performance bonuses to reward improving quality of care as part of a strategy to control spending growth and improve value.
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One of the earliest attempts to innovate in this area is the Alternative Quality Contract (AQC), established in 2009 by Blue Cross Blue Shield of Massachusetts (BCBSMA). The AQC pays provider organizations via a risk-adjusted prospective payment for all primary and specialty care provided to a population (that is, the global payment or budget) for a fiveyear period. AQC organizations are eligible for bonuses, in the initial years of up to 10 percent of their budget and later as a per member per month amount, based on performance on sixty-four outpatient and hospital measures. The measures are described in detail in the online Appendix A.[2] As of 2015 twenty large provider organizations, representing 90 percent of BCBSMA network primary care physicians and caring for more than 650,000 enrollees, are covered under this arrangement. Research on changes in spending and quality under the AQC has found that the initiative was associated with a slower rate of total spending growth (driven primarily by a shifting of outpatient care to providers with lower fees and reduced use of procedures, imaging, and tests) and improved ambulatory care quality compared to BCBSMA members not enrolled in the AQC.[3–5]
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No information is available on how this model affects care for people with mental illness. Mental illness often goes undetected or undertreated in primary care, and mental health services delivered across primary and specialty care are often poorly coordinated under feefor-service arrangements.[6] The AQC model could improve mental health care by giving providers an incentive to bridge the historical separation of mental health financing and delivery from the rest of health care delivery. Also, mental health conditions are highly comorbid with other chronic conditions, such as cardiovascular disease, diabetes, and cancer.[7–10] Better integration could improve treatment of both mental health and other conditions among individuals with a mental health conditions, who often receive worse care for physical health conditions than the general population.[11,12] Finally, prior research suggests that better mental health care holds promise for improving the value of health care expenditures.[13–15]
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However, provider organizations at risk for a population’s total costs may avoid enrolling or underprovide services to high-cost individuals such as those with mental illness.[16] Bonuses for meeting performance measures are expected to encourage high-quality care and prevent stinting of care. The contracts for the period we studied included only two mental health measures, both related to antidepressant medication use. Importantly, the AQC’s effects on individuals with mental illness may depend in part on whether mental health services are included in an AQC organization’s risk contract. Only a subset of initial five-year AQC contracts (five of twelve) included risk for behavioral health (i.e., that is, mental health and substance use disorder) specialty service use.[17] (We do not examine use of substance use disorder services which, like mental health services, were included in a subset of the initial five-year contracts.)
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This study used a mixed methods approach. First, with BCBSMA claims data, we examined whether AQC implementation affected mental health service use, mental health spending, and total health care spending among enrollees diagnosed with mental health conditions. For all utilization and spending outcomes, we compared two intervention groups–one that included enrollees in AQC organizations that accepted behavioral health risk and one that included enrollees in AQC organizations that did not–to a comparison group of BCBSMA enrollees not participating in the AQC.
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Second, given prior evidence of worse chronic care management among individuals with mental health conditions compared with others,[11,12] we examined how the AQC affected five performance metrics for diabetes and cardiovascular disease among people with these conditions and a co-occurring mental health condition. Third, we interviewed leaders in both AQC organizations and specialty mental health provider organizations to gauge perceptions about the impact of the AQC on enrollees with mental illness.
Study Data And Methods Administrative Data And Study Population We used 2006–11 inpatient, outpatient, and pharmacy claims data from Blue Cross Blue Shield of Massachusetts. Our study population included adults ages 18–64 who were enrolled in a BCBSMA health maintenance organization or point-of-service plan, all of whom were required to select a primary care physician upon enrollment. A BCBSMA enrollee was eligible for the Alternative Quality Contract group if his or her primary care physician belonged to an organization that had entered the contract.
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Provider organizations could enter the AQC on a rolling basis in 2009, 2010, or 2011. We used a difference-in-differences design that included in the comparison group both enrollees in organizations that, in a specific calendar year, had not yet entered the AQC but would in a future year, and enrollees in organizations that never entered the AQC. The difference-indifferences approach allowed us to account for differences in the characteristics of enrollees across AQC and non-AQC organizations and secular trends in service use unrelated to the AQC.
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Identifying Mental Health Service Users
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We employed a common approach to identifying mental health service users in insurance claims.[18,19] First, we classified enrollees as mental health service users based on diagnostic codes 290, 293–302, and 306–316 in the International Classification of Diseases, Ninth Revision, Clinical Modification. An enrollee was considered an inpatient mental health user if the last primary discharge diagnosis and the majority of all primary diagnoses during an inpatient admission were mental health diagnoses. An enrollee was considered an outpatient user if he or she had a mental health primary diagnosis on an outpatient claim. The unit of analysis was the person-calendar year. An individual’s data for a given year was used if he or she was enrolled for medical, behavioral, and pharmacy benefits during all twelve months. In counting mental health spending and use, we included all mental health inpatient claims as defined above, all outpatient claims with a mental health primary diagnosis or a Current Procedural Terminology or Healthcare Common Procedure Coding System code specific to mental health treatment, and all psychotropic prescription claims for individuals identified as mental health users. Measures We first examined the relationship between the AQC and the probability of using mental health services. We then assessed, among mental health service users, the relationship between the AQC and average mental health spending, total health care spending, number of inpatient mental health days, number of outpatient mental health visits, number of psychotropic medication management visits, number of psychotherapy visits, and number of thirty-day-equivalent psychotropic prescriptions.
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For enrollees with diabetes or cardiovascular conditions with and without a co-occurring mental health condition, we examined four Healthcare Effectiveness Data and Information Set (HEDIS) diabetes management measures–LDL cholesterol screening, HbA1C testing, monitoring for nephropathy (kidney disease as a result of diabetes), and retinal exam–and one HEDIS cholesterol management measure–LDL cholesterol screening–following HEDIS specifications. We compared these metrics for AQC and non-AQC enrollees. Statistical Analyses
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We used two-part, difference-in-differences models to estimate changes in the probability of mental health spending and use attributable to the AQC. In the second stage, we estimated the AQC’s effect on spending using linear regression.[20] For intensity of use outcomes, we estimated negative binomial regression models. We used logistic regression to examine the probability of mental health service use and of meeting HEDIS-based performance metrics for management of diabetes and cardiovascular conditions. We estimated two main regression models: the overall effects of the AQC and the effects among enrollees in AQC organizations that did and did not accept behavioral health risk. We estimated a third set of regression models including cohort indicators to examine whether the effects of the AQC differed based on how many years the enrollee’s provider organization had been subject to the AQC. For example, in 2011 some enrollees had been part of an AQC organization for one year (that is, enrollees in organizations that entered the
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AQC in 2011) while other enrollees had been in AQC organizations for multiple years (that is, two years for enrollees in organizations that entered in 2010 and three years for enrollees in organizations that entered in 2009). In all models, we controlled for sex, age category (18–27, 28–37, 38–47, 48–57, and 58–64), gender*age, risk score (calculated by BCBSMA from current-year diagnoses, claims, and demographic information using the diagnostic cost group scoring system DxCG Risk Analytics from Verisk Health), AQC group cohort dummies (that is, whether the enrollee’s provider organization entered the AQC in 2009, 2010, or 2011), and calendar-year dummies. In all regression analyses, we clustered standard errors at the practice level. The magnitude of the results can be interpreted as the average annual change in outcome over the study period if all enrollees were subject to the AQC versus if all enrollees were not subject to the AQC.
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Given that our design was quasi-experimental, we used propensity score weighting to minimize selection bias.[21] In particular, “inverse probability of treatment weights” were created for each calendar year, equating the AQC risk, AQC no-risk, and comparison groups on observed covariates in each year.[22] Covariates in the propensity score models were age group, sex, and the risk score. Analyses using the mental health service users only used weights estimated among that subgroup. The HEDIS diabetes and cardiovascular analyses that used all enrollees with diabetes and cardiovascular conditions used weights estimated among that larger group.
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We ran several sensitivity analyses. In addition to our primary spending model using linear regression (a decision based on model fit diagnostics), we estimated spending using log transformed and gamma models. For outcomes examining quantity of services used, we also ran a Poisson regression as an alternative to negative binomial regression. Results for sensitivity analyses were nearly identical to the main results. See results available in Appendix B.[2] Semistructured Interviews
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We conducted semistructured interviews with senior administrative and clinical leaders at AQC organizations and specialty mental health provider organizations in Massachusetts to assess perceptions about how the AQC is affecting care for individuals with mental illness. We developed interview guides for AQC participants and specialty provider organization participants. Guides began with broad questions to elicit participants’ open descriptions of AQC implementation and potential impacts on enrollees with mental illness and then turned to specific questions regarding strategies, facilitators, and barriers to altering quality or spending for this group. We included qualitative data collection in this study to contextualize our empirical research and aid in interpretation of findings since AQC implementation involves complex organizational behavior.[23] We interviewed a total of twenty-five individuals with eighteen participants interviewed from eight AQC organizations (three that accepted behavioral health risk and five that did not), and seven participants interviewed from large specialty mental health provider organizations. All interviews were conducted between February and June 2014 by teams of two to four study authors with participant consent obtained. Detailed field notes were compiled and used to identify themes and findings.
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Limitations
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A number of study limitations are worth noting. First, given that we studied a single global payment and accountable care model affecting the privately insured enrollees of one insurer (albeit the largest) within a single state, our results may not be generalizable to other private or public payers or populations. Second, entrance into the AQC program was voluntary, and it is critical to consider the extent to which providers or enrollees in participating programs differed from those in nonparticipating programs. When comparing the trends observed for each AQC cohort during their pre-AQC time period to the trends for provider groups that had not entered the AQC by that time, we see no evidence of differential trends in the probability of mental health service use nor in total health care or mental health care spending among mental health service users. Even if there were slight differences in those trends, the estimation procedure would account for these pre-period differences by assuming they would have persisted. Third, our data end in 2011, and we did not study effects through the full life of the initial five-year AQC contracts; this choice was necessary to preserve a viable control group. Fourth, while organizations that did and did not accept behavioral health risk did not appear to systematically differ in obvious ways (for example, size or level of integration), it is possible that there were unobserved differences. Fifth, because of relatively small sample sizes, we may have lacked statistical power to detect small differences in HEDIS indicators for diabetes and cardiovascular care for mental health service users. Finally, while data provide rich detail about an enrollee’s service use, they are limited in their ability to convey important clinical detail. The claims data component of this study was determined to be exempt, and the interview data collection was determined to not be human subjects research by the Harvard Medical School and Johns Hopkins Bloomberg School of Public Health Institutional Review Boards.
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Study Results Usage And Spending
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(Exhibit 1). We found a significant but very small decrease in the probability of using mental health services (−1.41 percentage points) among BCBSMA enrollees in AQC organizations relative to the comparison group. This decline was concentrated among enrollees in AQC organizations facing behavioral health risk (−2.09 percentage points) (Exhibit 2). We found no difference in the probability of using mental health services in the no-risk group relative to the comparison group. We found no effect of the AQC on mental health spending for mental health service users overall or in the AQC behavioral health risk or no-risk groups. However, a 1 percent annual decline attributable to the AQC in total health care spending was detected among mental health service users overall (−$189), and among enrollees in the behavioral health risk group (−$238) (the decrease in the no-risk group was not significant) (Exhibit 2). Results in Exhibit 3 on annual changes in the average quantity of mental health services used attributable to the AQC were consistent with spending results. No differences in average inpatient mental health days among users were detected. We found no relationship between the AQC and the quantity of outpatient mental health visits among users overall.
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For the analysis separating out the effect on AQC organizations that did and did not accept behavioral health risk, we identified a statistically significant annual increase in outpatient visits among users in the no-risk group (0.51 visits). We found a significant, but small, increase in the quantity of medication management visits overall (0.08 visits) and within the risk group (0.09 visits). The AQC had no effect on the quantity of psychotherapy visits among service users overall. However, we found a statistically significant increase in psychotherapy visits among users in the no-risk group (0.48 visits). The AQC led to an increase in the average number of thirty-day-equivalent psychotropic medication prescriptions overall (0.23), and in the no-risk group (0.34). Sensitivity analyses in Appendix B looking at the effects of the AQC on use and spending by years of exposure did not suggest differential patterns by the years a provider organization had been an AQC organization.[2]
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Diabetes And Cardiovascular Disease Performance Measures
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Exhibit 4 indicates that non-mental health service users experienced statistically significant improvements attributable to the AQC in three of five performance measures studied (nephropathy monitoring, LDL screening, and retinal exam, among individuals with diabetes). While other research has shown that individuals with mental illness often receive poorer quality care for chronic medical conditions than the general population,[11,12] this does not appear to be the case among BCBSMA enrollees. Rates of compliance with diabetes and cardiovascular performance metrics in the non-AQC group among mental health service users were comparable to, and in two specific cases (that is, nephropathy monitoring and retinal exam) very slightly higher than, non-mental health service users in the non-AQC group (Exhibit 4). However, it appears that non-mental health service users improved more under the AQC compared with mental health users, who appear to have experienced no statistically significant increases in rates. For two measures, nephropathy monitoring (difference-in-differences −2.90; p value = 0.01) and retinal exam (difference-indifferences −2.57; p value = 0.02), the p value of the interaction indicates that non-mental health users benefited more from the AQC than mental health users, although from a clinical standpoint, the changes observed were quite small in magnitude. Perceptions About Effects Of The AQC
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Consistent with the quantitative findings described above, the central message conveyed from interview participants was that the AQC had not yet meaningfully affected the provision of mental health care to enrollees during the first three contract years. There was a general recognition among most participants that many of the enrollees with the highest total health care costs had mental health conditions and that their organizations needed to do a better job integrating behavioral health to perform well on the contract. However, the overarching view was that little progress had been made with regard to mental health integration during the contract’s initial years, and delivery system changes that would facilitate behavioral health integration were viewed as a longer-term objective. Among specialty mental health organizations, there was low awareness about the AQC. Participants noted several possible contributors to the minimal initial impact of the AQC on mental health care. Participants emphasized the limited capacity of the AQC mental health
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performance measures to shift behaviors. For AQC organizations, BCBSMA enrollees covered under the AQC constituted a relatively small share of their total patient populations– typically less than 15 percent. Given that there were so many performance metrics both within the AQC and across other contracts (including Medicare Pioneer and other commercial contracts), organizations ended up focusing on a small subset of measures that were common across contracts and perceived as “movable,” meaning that leaders felt they could improve performance on the measures with a reasonable amount of effort; other measures were largely ignored. None of the AQC leaders interviewed reported that their organizations had focused on the two mental health antidepressant continuation measures. Participants reported that clinicians in their organizations did not view these metrics as meaningful indicators of quality. Also, there was a three-month lag in receipt of prescription data by the AQC organizations, rendering the antidepressant measures “not actionable” with physicians. Also, these measures were viewed as applying to a relatively small number of patients compared with, for example, a mental health screening and follow-up measure (not included in the initial contracts but added in 2013 as a test measure), which was considered more promising.
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Second, in the face of dramatic change in financing and delivery created by the AQC, many leaders reported that their organizations were focused initially on improving “code capture” to ensure that they were accounting for what was already occurring within the organization related to performance metrics and were beginning to put in place new processes (for example, registries and reminder systems) to improve performance scores that primarily affected other clinical areas. Near the end of the first five-year contract period, participants noted that several organizations were in the early implementation stages of efforts to improve integration, including pilot programs to embed behavioral health clinicians into primary care practices and targeted programs focused on improving care for individuals with co-occurring depression and physical health conditions. One AQC organization mentioned that it had had recently instituted a pilot program involving chronic care case management for enrollees with diabetes and co-occurring depression that included a mental health professional on the diabetes care management team. Several early efforts relied on grant funding to cover salaries of new personnel (for example, social workers embedded in case management teams) and implementation costs. One stakeholder noted that the confluence of risk-based contracting across payers from 2012 to 2014 helped to justify these infrastructure investments to organization leadership.
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Finally, limited mental health provider capacity was viewed as a major barrier to change under the AQC. While prior research indicated that about half of the overall savings to date from the AQC has come from referrals to lower-cost providers, AQC organizations indicated that this strategy was less viable in the mental health context given the shortage of mental health clinicians accepting commercial insurance.
Discussion New payment and delivery system innovations are proliferating under the ACA and statelevel reforms, and these models have the potential to address some long-standing concerns about care for mental illness. In this study, we examined how one model launched in
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Massachusetts–the Alternative Quality Contract–affected access to mental health services, the quantity of services used, mental health spending, and stakeholder perceptions of the AQC’s effect on mental health service delivery. Our findings suggest a small decrease in the probability of using mental health services attributable to the AQC concentrated among enrollees in AQC organizations accepting risk for behavioral health. We found no effects on mental health spending among users, but total health care spending among this group did decrease consistent with prior findings in the full enrollee population.[3–5] As noted above, prior studies found that overall spending decreases were accompanied by a shift in referrals to lower-cost providers. Shortages among mental health providers or weak referral networks might explain why no reductions were found in mental health spending among users. In the presence of these shortages, a substantial reduction in mental health spending could have signaled possible stinting.
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We found changes in the quantity of services received among mental health users in the norisk group, including relatively small increases in the number of outpatient services, psychotherapy visits, and psychotropic prescriptions. There was less evidence for effects on quantity of services used among the risk group, although small increases in medication management were found. Since BCBSMA manages inpatient care for all provider groups (AQC or not), it is perhaps not surprising that no changes in inpatient use were detected.
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The AQC did not lead to significant improvements in performance metrics for diabetes or cardiovascular disease among enrollees with co-occurring mental health service use, although performance rates in the absence of the AQC approached or exceeded 90 percent for all measures but one (retinal exams), leaving only limited room for improvement. For two measures–nephropathy monitoring and retinal exam–it appears that non-mental health users benefited more from the AQC than mental health users. Given research showing serious chronic disease risks including high rates of premature cardiac death[24] and diabetes-related complications[25] among people with mental illness, population-based incentives such as those in the AQC might not be sufficient for addressing the needs of this highly vulnerable group. More targeted monitoring and customized interventions may be needed to better manage chronic diseases among the subgroup with co-occurring mental illness.
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Qualitative study results indicate that participants perceived the AQC to have had a minimal impact on care for mental health during the study period, achieving little progress toward integration of mental health and physical health care. However, several AQC leaders mentioned efforts in the planning and early implementation phase to improve integration, particularly for individuals with co-occurring mental and physical health conditions. These qualitative findings are consistent with the results from the claims-based quantitative analyses and with results from a national survey of accountable care organizations, which found that only 14 percent of respondents reported that integration of behavioral health and primary care was complete or nearly complete.[26] Prior research indicates that the AQC led to an average annual increase of 2.9 percentage points in the two-year period following AQC implementation for one of the two antidepressant measures tied to bonuses.[5] One possible explanation mentioned in multiple
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interviews was that the increase could have been as a result of improved coding since programs aimed at improving depression care performance had not yet been instituted by AQC organizations. Lack of focus on improving performance on the mental health measures reported by interviewees raises questions about performance incentives in risk-based contracts. A global payment model such as the AQC places strong financial incentives on provider organizations to improve the efficiency of care and control spending growth. These models rely on performance incentives to discourage stinting and promote high-quality care. Given the numerous dimensions of performance improvement and the goal of measuring it broadly across many subpopulations and types of services, it is not surprising that only two of the AQC’s sixty-four measures–both well-established, validated measures–were devoted to behavioral health. However, the fact that providers did not feel these measures were meaningful indicators of mental health care quality and felt that the barriers to improvement on these metrics were too great underscores the challenges payers face in designing performance incentive systems given the lack of widely accepted behavioral health performance measures. The behavioral health field must continue to develop new, meaningful measures capturing multiple dimensions of performance with respect to behavioral health care delivery to ensure that measurement systems in risk-based contracts achieve their intended goals.[27]
Conclusion
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This study provides the first empirical examination of how this new model of paying for and delivering care affects individuals seeking mental health treatment. As accountable care evolves, it will be critical for payers considering these models, as well as providers operating under these contracts, to understand how they affect care for often high-cost individuals with mental health treatment needs.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
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Preliminary results were presented at the National Institute of Mental Health’s Mental Health Services Research Conference, in Bethesda, Maryland, in April 2014, and at the Association for Public Policy Analysis and Management’s Fall Research Conference, in Albuquerque, New Mexico, in November 2014. The authors acknowledge funding support from the Commonwealth Fund (Grant No. 20130499; Multi-PI: Colleen Barry and Haiden Huskamp). Shelly Greenfield acknowledges support from the National Institute on Drug Abuse (Grant No. K24 DA019855). Robert Mechanic is a trustee at Atrius Health, one of the provider organizations that signed the Alternative Quality Contract with Blue Cross Blue Shield of Massachusetts (BCBSMA). The authors thank Dana Gelb Safran at BCBSMA and Jeffrey Simmons, formerly of BCBSMA, for their support of the project, and Christina Fu and Hocine Azeni of Harvard Medical School for expert programming support. The authors also thank Alisa B. Busch for her assistance in developing mental health diagnostic cohorts and comorbidity categories, as well as mental health treatment categories derived from procedure codes.
Biographies Colleen L. Barry ([email protected]) is an associate professor and associate chair for research and practice in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, in Baltimore, Maryland.
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Elizabeth A. Stuart is a professor in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health. Julie Marie Donohue is an associate professor in the Department of Health Policy and Management at the University of Pittsburgh Graduate School of Public Health, in Pennsylvania. Shelly F. Greenfield is a professor of psychiatry at McLean Hospital, in Belmont, Massachusetts. Elena Kouri is project director in health care policy at Harvard Medical School, in Boston, Massachusetts.
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Kenneth Duckworth is medical director for behavioral health at Blue Cross Blue Shield of Massachusetts, in Quincy. Zirui Song is a physician in the Department of Medicine at Massachusetts General Hospital, in Boston. Robert E. Mechanic is a senior fellow at the Heller School for Social Policy and Management, Brandeis University, in Waltham, Massachusetts. Michael E. Chernew is a professor in the Department of Health Care Policy at Harvard Medical School.
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Haiden A. Huskamp is a professor in the Department of Health Care Policy at Harvard Medical School.
Notes
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1. Berwick DM, Nolan TW, Whittington J. The Triple Aim: care, health, and cost. Health Aff (Millwood). 2008; 27(3):759–69. [PubMed: 18474969] 2. To access the Appendix, click on the Appendix link in the box to the right of the article online. 3. Song Z, Rose S, Safran DG, Landon BE, Day MP, Chernew ME. Changes in health care spending and quality 4 years into global payment. N Engl J Med. 2014; 371(18):1704–14. [PubMed: 25354104] 4. Song Z, Safran DG, Landon BE, He Y, Ellis RP, Mechanic RE, et al. Health care spending and quality in year 1 of the alternative quality contract. N Engl J Med. 2011; 365(10):909–18. [PubMed: 21751900] 5. Song Z, Safran DG, Landon BE, Landrum MB, He Y, Mechanic RE, et al. The ‘Alternative Quality Contract,’ based on a global budget, lowered medical spending and improved quality. Health Aff (Millwood). 2012; 31(8):1885–94. [PubMed: 22786651] 6. Edlund MJ, Unützer J, Wells KB. Clinician screening and treatment of alcohol, drug, and mental problems in primary care: results from Healthcare for Communities. Med Care. 2004; 42(12):1158– 66. [PubMed: 15550795] 7. Ciechanowski PS, Katon WJ, Russo JE. Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Arch Intern Med. 2000; 160(21):3278–85. [PubMed: 11088090] 8. Singer S, Brown A, Einenkel J, Hauss J, Hinz A, Klein A, et al. Identifying tumor patients’ depression. Support Care Cancer. 2011; 19(11):1697–703. [PubMed: 20853171]
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9. Katon WJ. The comorbidity of diabetes mellitus and depression. Am J Med. 2008; 121(11 Suppl 2):S8–15. [PubMed: 18954592] 10. Carroll D, Phillips AC, Hunt K, Der G. Symptoms of depression and cardiovascular reactions to acute psychological stress: evidence from a population study. Biol Psychol. 2007; 75(1):68–74. [PubMed: 17196733] 11. McGinty EE, Baller J, Azrin ST, Juliano-Bult D, Daumit GL. Quality of medical care for persons with serious mental illness: a comprehensive review. Schizophr Res. 2015; 165(2–3):227–35. [PubMed: 25936686] 12. Kilbourne AM, Welsh D, McCarthy JF, Post EP, Blow FC. Quality of care for cardiovascular disease-related conditions in patients with and without mental disorders. J Gen Intern Med. 2008; 23(10):1628–33. [PubMed: 18626722] 13. Chiles JA, Miller AL, Crismon ML, Rush AJ, Krasnoff AS, Shon SS. The Texas Medication Algorithm Project: development and implementation of the schizophrenia algorithm. Psychiatr Serv. 1999; 50(1):69–74. [PubMed: 9890582] 14. Mumford E, Schlesinger HJ, Glass GV, Patrick C, Cuerdon T. A new look at evidence about reduced cost of medical utilization following mental health treatment. Am J Psychiatry. 1984; 141(10):1145–58. [PubMed: 6435457] 15. Katon WJ, Russo JE, Von Korff M, Lin EH, Ludman E, Ciechanowski PS. Long-term effects on medical costs of improving depression outcomes in patients with depression and diabetes. Diabetes Care. 2008; 31(6):1155–9. [PubMed: 18332158] 16. Frank, RG.; McGuire, TG. Economics and mental health. In: Culyer, AJ.; Newhouse, JP., editors. Handbook of health economics. 1. Vol. 1. Cambridge (MA): National Bureau of Economic Research; 1999. p. 893-954. 17. All second-round five-year contracts include risk for behavioral health service use. 18. Goldman HH, Frank RG, Burnam MA, Huskamp HA, Ridgely MS, Normand SL, et al. Behavioral health insurance parity for federal employees. N Engl J Med. 2006; 354(13):1378–86. [PubMed: 16571881] 19. McConnell KJ, Gast SH, Ridgely MS, Wallace N, Jacuzzi N, Rieckmann T, et al. Behavioral health insurance parity: does Oregon’s experience presage the national experience with the Mental Health Parity and Addiction Equity Act? Am J Psychiatry. 2012; 169:31–8. [PubMed: 21890792] 20. Buntin MB, Zaslavsky AM. Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. J Health Econ. 2004; 23(3):525–42. [PubMed: 15120469] 21. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983; 70(1):41–55. 22. Stuart EA, Huskamp HA, Duckworth K, Simmons J, Song Z, Chernew M, et al. Using propensity scores in difference-in-differences models to estimate the effects of a policy change. Health Serv Outcomes Res Methodol. 2014; 14(4):166–82. [PubMed: 25530705] 23. Patton, MQ., editor. Qualitative research and evaluation methods. third. Thousand Oaks (CA): Sage Publications; 2001. 24. Newcomer JW, Hennekens CH. Severe mental illness and risk of cardiovascular disease. JAMA. 2007; 298(15):1794–6. [PubMed: 17940236] 25. Frayne SM, Halanych JH, Miller DR, Wang F, Lin H, Pogach L, et al. Disparities in diabetes care: impact of mental illness. Arch Intern Med. 2005; 165(22):2631–8. [PubMed: 16344421] 26. Lewis VA, Colla CH, Tierney K, Van Citters AD, Fisher ES, Meara E. Few ACOs pursue innovative models that integrate care for mental illness and substance abuse with primary care. Health Aff (Millwood). 2014; 33(10):1808–16. [PubMed: 25288427] 27. Goldman ML, Spaeth-Rublee B, Pincus HA. Quality indicators for physical and behavioral health care integration. JAMA. 2015; 314(8):769–70. [PubMed: 26043185]
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Exhibit 1
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Unadjusted Descriptive Statistics For Adult Blue Cross Blue Shield Of Massachusetts (BCBSMA) Enrollees Using Mental Health Services In The Comparison And Alternative Quality Contract (AQC) Groups Across Person-Years, 2006–11 Comparison groupa
Total AQC group
AQC group with behavioral health riskb
AQC group without behavioral health risk
2,999,221
533,568
236,542
297,026
51.67%
52.75%
51.02%
54.13%
18–27
17.08%
17.26%
15.66%
18.54%
28–37
17.58
17.84
16.72
18.73
38–47
24.96
23.96
24.28
23.71
48–57
26.15
26.17
27.29
25.27
58–64
14.24
14.77
16.05
13.75
Any co-occurring substance use disorder service use
1.63%
1.99%
1.93%
2.04%
1.24
1.26
1.31
1.22
Any mental health service use
14.44%
16.13%
14.87%
17.13%
Any inpatient mental health days
0.23%
0.26%
0.25%
0.27%
Average number of inpatient mental health days among inpatient mental health service users
11.92
12.74
13.29
12.34
Any outpatient mental health visits
9.84%
10.17%
10.27%
10.10%
Average number of outpatient mental health visits among outpatient mental health service users
9.44
9.50
9.55
9.46
Average number of medication management visits among outpatient mental health service users
0.93
0.84
0.84
0.84
Average number of psychotherapy visits among outpatient mental health service users
7.46
7.80
7.85
7.77
Any psychotropic medication use
9.19%
10.26%
9.38%
10.95%
Average number of thirty-day-equivalent psychotropic prescriptions among psychotropic medication users (mean)
12.98
13.21
13.13
13.30
Average mental health spending among mental health service users
$3,151
$2,929
$2,918
$2,937
Average total health care spending among mental health service users
$7,783
$7,638
$7,979
$7,412
Number of individuals (person-year) Female Age (years)
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Risk score (mean) Mental health service use in a calendar year
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Spending in a calendar year
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SOURCE BCBSMA claims data, 2006–11. NOTES Cost estimates have been adjusted for inflation.
a
BCBSMA enrollees not participating in the AQC.
b
“behavioral health risk”
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Exhibit 2
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Adjusted Annual Probability Of Use And Average Spending By Blue Cross Blue Shield Of Massachusetts (BCBSMA) Enrollees With And Without The Alternative Quality Contract (AQC) Across Person-Years, 2006–11
Author Manuscript
With AQC
Without AQC
Difference
95% CI
Probability of mental health use
16.04%
17.45%
−1.41%***
−2.06, −0.76
Behavioral health risk
15.21%
17.30%
−2.09%***
−3.29, −0.99
No behavioral health risk
17.14%
17.30%
−0.16%
−1.27, 0.95
Average mental health spending conditional on mental health use
$3,063
$3,078
−$15
−61, 91
Behavioral health risk
$3,030
$3,056
−$26
−126, 75
No behavioral health risk
$3,134
$3,056
$78
−51, 208
Average total health care spending conditional on mental health use
$8,137
$8,316
−$189**
−368, −9
Behavioral health risk
$8,078
$8,316
−$238**
−468, −9
No behavioral health risk
$8,207
$8,316
−$109
−300, 81
SOURCE BCBSMA claims data, 2006–11. NOTES CI is confidence interval. Two-part models adjusted for sex, age category, risk score, year, and AQC cohort and estimated using propensity score weights. Cost estimates have been adjusted for inflation. Difference-in-differences estimation used to account for secular trends.
**
p < 0.05
***
p < 0.01
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Exhibit 3
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Adjusted Annual Quantity Of Mental Health Services Used By Blue Cross Blue Shield Of Massachusetts (BCBSMA) Enrollees With And Without The Alternative Quality Contract (AQC) Across Person-Years, 2006–11
Author Manuscript
With AQC
Without AQC
Difference
95% CI
Average number of inpatient mental health days conditional on inpatient mental health use
12.52
12.13
0.39
−1.07, 1.86
Behavioral health risk
12.50
12.13
0.37
−1.35, 2.08
No behavioral health risk
12.55
12.13
0.42
−1.08, 1.92
Average number of outpatient mental health visits conditional on outpatient mental health use
9.54
9.35
0.19
−0.17, 0.55
Behavioral health risk
9.31
9.32
−0.01
−0.52, 0.51
No behavioral health risk
9.82
9.32
0.51**
0.04, 0.95
Average number of medication management visits conditional on outpatient mental health use
0.94
0.86
0.08***
0.03, 0.13
Behavioral health risk
0.95
0.86
0.09**
0.02, 0.17
No behavioral health risk
0.92
0.86
0.06
−0.01, 0.13
Average number of psychotherapy visits conditional on outpatient mental health use
7.01
6.93
0.08
−0.27, 0.44
Behavioral health risk
6.73
6.89
−0.16
−0.68, 0.35 0.02, 0.91
No behavioral health risk
7.37
6.89
0.48**
Average number of thirty-day-equivalent psychotropic medication prescriptions conditional on psychotropic medication use
13.21
12.98
0.23***
0.03, 0.42
Behavioral health risk
13.13
12.97
0.16
−0.09, 0.40
No behavioral health risk
13.31
12.97
0.34***
0.09, 0.58
Author Manuscript
SOURCE BCBSMA claims data, 2006–11. NOTES CI is confidence interval. All quantity of service use outcomes conditional on an individual being a mental health service user. Negative binomial models adjusted for sex, age category, risk score, year, and AQC cohort and estimated using propensity score weights. Cost estimates have been adjusted for inflation. Difference-in-differences estimation used to account for secular trends.
**
p < 0.05
***
p < 0.01
Author Manuscript Health Aff (Millwood). Author manuscript; available in PMC 2016 December 01.
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Author Manuscript 91.55 62.99
HbA1C test
Retinal exam
87.31
61.45
90.40
87.45
90.36
Without AQC (%)
0.67
1.54
1.15
0.89
−0.94
AQC difference (%)
91.23
64.74
92.49
90.05
91.45
With AQC (%)
90.06
60.63
91.83
88.29
89.49
Without AQC (%)
−0.50
−2.57**
4.11***
1.17
0.49
−0.87
1.76*** 0.66
−2.90***
Difference in differences (%)
1.96***
AQC difference (%)
Non-mental health service users
p < 0.01
p < 0.05
***
**
SOURCE BCBSMA claims data, 2006–11. NOTES Logistic regression models adjusted for sex, age category, risk score, year, and AQC cohort and estimated using propensity score weighting. Differencein-differences estimation used to account for secular trends. Numbers for mental health users and non-mental health users, respectively: diabetes management: 26,113 and 157,023; cardiovascular disease management: 7,209 and 49,622. Non-mental health users are the referent group.
LDL cholesterol screening
87.98
88.34
LDL cholesterol screening
Cardiovascular disease management
89.42
With AQC (%)
Monitoring for nephropathy
Diabetes management
Condition management indicators
Mental health service users
Adjusted Annual Changes In Probability Of Blue Cross Blue Shield Of Massachusetts (BCBSMA) Enrollees Meeting Healthcare Effectiveness Data And Information Set Diabetes And Cardiovascular Conditions Management Indicators With And Without The Alternative Quality Contract (AQC), By Mental Health User Status, 2006–11
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Exhibit 4 Barry et al. Page 16
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